1 citations · 2 across the 9 of their papers we have counts for
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C3Net: Context-Contrast Network for Camouflaged Object Detection
Baber Jan, Aiman H. El-Maleh, Abdul Jabbar Siddiqui +2
Camouflaged object detection identifies objects that blend seamlessly with their surroundings through similar colors, textures, and patterns. This task challenges both traditional…
MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection
Leena Alghamdi, Muhammad Usman, Hafeez Anwar +2
Camouflaged object detection is an emerging and challenging computer vision task that requires identifying and segmenting objects that blend seamlessly into their environments due…
SPEGNet: Synergistic Perception-Guided Network for Camouflaged Object Detection
Baber Jan, Saeed Anwar, Aiman H. El-Maleh +2
Camouflaged object detection segments objects with intrinsic similarity and edge disruption. Current detection methods rely on accumulated complex components. Each approach adds co…
SAM-PTx: Text-Guided Fine-Tuning of SAM with Parameter-Efficient, Parallel-Text Adapters
Shayan Jalilian, Abdul Bais
The Segment Anything Model (SAM) has demonstrated impressive generalization in prompt-based segmentation. Yet, the potential of semantic text prompts remains underexplored compared…
Segmenting Visuals With Querying Words: Language Anchors For Semi-Supervised Image Segmentation
Numair Nadeem, Saeed Anwar, Muhammad Hamza Asad +1
Vision Language Models (VLMs) provide rich semantic priors but are underexplored in Semi supervised Semantic Segmentation. Recent attempts to integrate VLMs to inject high level se…
MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking
Numair Nadeem, Muhammad Hamza Asad, Saeed Anwar +1
Semantic segmentation of crops and weeds is crucial for site-specific farm management; however, most existing methods depend on labor intensive pixel-level annotations. A further c…